Aaron Sam A. S.
Shanmuga Pradeepan R.
Sobana R. S.
S. Rathnamala
A. Karthick Kumar
- Student, Department of Computer Science and Engineering, Sethu Institute of Technology, Virudhunagar, Tamil Nadu, India
- Student, Department of Computer Science and Engineering, Sethu Institute of Technology, Virudhunagar, Tamil Nadu, India
- Student, Department of Computer Science and Engineering, Sethu Institute of Technology, Virudhunagar, Tamil Nadu, India
- Associate Professor, Department of computer science and engineering, Sethu Institute of Technology, Virudhunagar, Tamil Nadu, India
- Assistant Professor, Department of Computer Science and Engineering, Sethu Institute of Technology, Virudhunagar, Tamil Nadu, India
Abstract
Accurate screw counting is essential in the manufacturing sector to ensure efficient inventory management and maintain quality control standards. The current manual counting method is prone to errors and lacks the ability to identify the source of missing screws. To address this challenge, we propose implementing an automated screw counting system at Indo Metal Tech in Ambattur, Chennai. This system would utilize advanced image processing and machine learning algorithms to detect and tally screws within trays captured by cameras. The algorithm incorporates user-triggered actions, real-time feedback, and user verification to ensure accuracy and adaptability to changing manufacturing conditions. This innovative solution not only improves efficiency and accuracy but also facilitates collaborative manufacturing practices. The system, equipped with a camera and sophisticated image processing algorithms, effectively and precisely handles these challenges. Additionally, the solution’s versatility is enhanced by QR codes for collaborative counting and the option for continuous counting between trays. This reduces human error rates and establishes a framework for enhanced traceability and quality control in the manufacturing process. Overall, this proposed solution not only meets the immediate need for precise screw tallying but also lays the groundwork for an improved, simplified, and collaborative manufacturing process.
Keywords: Computer vision, image processing, object detection, pre-trained models, screw counting
[This article belongs to Journal of Image Processing & Pattern Recognition Progress(joipprp)]
References
- Erhan D, Szegedy C, Toshev A, Anguelov D. Scalable object detection using deep neural networks. In Proceedings of the IEEE conference on computer vision and pattern recognition. 2014; 2147–2154.
- Dai J, Li Y, He K, Sun J. R-fcn: Object detection via region-based fully convolutional networks. NIPS’16: Proceedings of the 30th International Conference on Neural Information Processing Systems. 2016 Dec; 379–387.
- Joseph Redmon, Santosh Divvala, Ross Girshick. You Only Look Once: Unified, Real- Time Object Detection. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2016; 779–788.
- Redmon J, Farhadi A. YOLO9000: better, faster, stronger. In Proceedings of the IEEE conference on computer vision and pattern recognition. 2017; 7263–7271.
- Karen Simonyan, Andrew Zisserman. Very Deep Convolutional Networks for Large-Scale Image Recognition. Computer Vision and Pattern Recognition (cs.CV). 2014 Sep.
- Lichao Huang, Yi Yang, Yafeng Deng, Yinan Yu. DenseBox: Unifying Landmark Localization with End to End Object Detection. Computer Vision and Pattern Recognition (cs.CV). 2015 Sep.
- Blaschko Matthew B, Lampert Christoph H. Learning to Localize Objects with Structured Output Regression. Computer Vision, ECCV. 2008; 2–15.
- Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun. Faster R-CNN: Towards Real Time Object Detection with Region Proposal Networks. EEE Transactions on Pattern Analysis and Machine Intelligence. 1 June 2017; 39(6): 1137–1149.
- Wei Liu, Dragomir Anguelov, Dumitru Erhan. SSD: Single Shot MultiBox Detector. Computer Vision, ECCV. 2016; 21–37.
- Juan Du (New Research, and Development Center of Hisense, Qingdao 266071, China). Understanding of Object Detection Based on CNN Family and YOLO. J Phys Conf Ser. 2018 Apr; 1004(1): 012029.
Journal of Image Processing & Pattern Recognition Progress
| Volume | 11 |
| Issue | 01 |
| Received | March 11, 2024 |
| Accepted | March 20, 2024 |
| Published | April 3, 2024 |